{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://osloyi5le.bkt.clouddn.com/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%B7%A5%E7%A8%8B%E5%B8%88banner.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 波士顿房价-连续类型特征数据的回归问题-构建回归树-决策树\n",
    ">张子豪 同济大学研究生 2019-05-15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0.import工具库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.加载数据，认识数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston_house = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'filename', 'target']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(boston_house)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_house.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, 0.0000e+00, 5.3800e-01,\n",
       "        6.5750e+00, 6.5200e+01, 4.0900e+00, 1.0000e+00, 2.9600e+02,\n",
       "        1.5300e+01, 3.9690e+02, 4.9800e+00],\n",
       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, 0.0000e+00, 4.6900e-01,\n",
       "        6.4210e+00, 7.8900e+01, 4.9671e+00, 2.0000e+00, 2.4200e+02,\n",
       "        1.7800e+01, 3.9690e+02, 9.1400e+00],\n",
       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, 0.0000e+00, 4.6900e-01,\n",
       "        7.1850e+00, 6.1100e+01, 4.9671e+00, 2.0000e+00, 2.4200e+02,\n",
       "        1.7800e+01, 3.9283e+02, 4.0300e+00],\n",
       "       [3.2370e-02, 0.0000e+00, 2.1800e+00, 0.0000e+00, 4.5800e-01,\n",
       "        6.9980e+00, 4.5800e+01, 6.0622e+00, 3.0000e+00, 2.2200e+02,\n",
       "        1.8700e+01, 3.9463e+02, 2.9400e+00],\n",
       "       [6.9050e-02, 0.0000e+00, 2.1800e+00, 0.0000e+00, 4.5800e-01,\n",
       "        7.1470e+00, 5.4200e+01, 6.0622e+00, 3.0000e+00, 2.2200e+02,\n",
       "        1.8700e+01, 3.9690e+02, 5.3300e+00]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_house.data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "506"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(boston_house.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_house.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "506"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(boston_house.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print(boston_house.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston_feature_name = boston_house.feature_names\n",
    "boston_features = boston_house.data\n",
    "boston_target = boston_house.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_feature_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston_house.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, 0.0000e+00, 5.3800e-01,\n",
       "        6.5750e+00, 6.5200e+01, 4.0900e+00, 1.0000e+00, 2.9600e+02,\n",
       "        1.5300e+01, 3.9690e+02, 4.9800e+00],\n",
       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, 0.0000e+00, 4.6900e-01,\n",
       "        6.4210e+00, 7.8900e+01, 4.9671e+00, 2.0000e+00, 2.4200e+02,\n",
       "        1.7800e+01, 3.9690e+02, 9.1400e+00],\n",
       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, 0.0000e+00, 4.6900e-01,\n",
       "        7.1850e+00, 6.1100e+01, 4.9671e+00, 2.0000e+00, 2.4200e+02,\n",
       "        1.7800e+01, 3.9283e+02, 4.0300e+00],\n",
       "       [3.2370e-02, 0.0000e+00, 2.1800e+00, 0.0000e+00, 4.5800e-01,\n",
       "        6.9980e+00, 4.5800e+01, 6.0622e+00, 3.0000e+00, 2.2200e+02,\n",
       "        1.8700e+01, 3.9463e+02, 2.9400e+00],\n",
       "       [6.9050e-02, 0.0000e+00, 2.1800e+00, 0.0000e+00, 4.5800e-01,\n",
       "        7.1470e+00, 5.4200e+01, 6.0622e+00, 3.0000e+00, 2.2200e+02,\n",
       "        1.8700e+01, 3.9690e+02, 5.3300e+00]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_features[:5,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rgs = tree.DecisionTreeRegressor(max_depth=4)\n",
    "rgs = rgs.fit(boston_features, boston_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=4, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pydotplus\n",
    "from IPython.display import Image, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dot_data = tree.export_graphviz(rgs,\n",
    "                                out_file = None,\n",
    "                                feature_names = boston_feature_name,\n",
    "                                class_names = boston_target,\n",
    "                                filled = True,\n",
    "                                rounded = True\n",
    "                               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jz8/Pzs5OIpF8hlu/fPly6dKlM6ZPV1dTdXPs5NihbaM6NT7PrUsO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\n1Pj6+paUlDQ5MwAA/BhmZmYbN24sKiqqbejTbd2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WEtLR1PhzzRIH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zn9b+MgYAo9ZSufEIoQdvSzuXvC2u+VDRaKgoRMDjHO6mbw5MzCytXSo3fouaBBczXcCLgm1X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T7y6nUJFyenirKSl6fH1Cmjp4r6B5xobWtzc933aydCEAT5hxiqLLBeoQAASnOF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\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719hKjsqrsJ4k3W0e8hWGYcTNZl/mIfvc9/iNeUh+jDEPOfX7Fyy/Og9Z2ONxQ/9HAjyE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pB09mAffGwxl2UnJyPhYaQs2ikPAAgCMW8adcVY8BYdAKCnIvPRnud6o+xFVbv1oiHiFYtDaloJ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MtGGv67ZFF43cHmlaun8svfThVlb9QhE2WwWBlu4f9ncLr2SMjyNb1twLACKcjJ19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\nAAAAAAAArAL/BZSMkburtzGAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "display(Image(graph.create_png()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 预测新样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a1 = boston_features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.42727273])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs.predict(a1.reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a2 = boston_features[1]\n",
    "a3 = boston_features[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "b =np.row_stack((a1,a2,a3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.42727273, 21.62974359, 32.74878049])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs.predict(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.320e-03, 1.800e+01, 2.310e+00, 0.000e+00, 5.380e-01, 6.575e+00,\n",
       "       6.520e+01, 4.090e+00, 1.000e+00, 2.960e+02, 1.530e+01, 3.969e+02,\n",
       "       4.980e+00])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a4 = np.array([0.000e+00, 5.000e+01, 2.310e+00, 0.000e+00, 0.580e-01, 9.075e+00,\n",
    "       6.520e+01, 1.090e+01, 1.000e+00, 2.960e+02, 15.30e+02, 0.969e+02,\n",
    "       4.980e+00])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43.65333333])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs.predict(a4.reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.42727273, 21.62974359, 32.74878049, 32.74878049, 32.74878049,\n",
       "       21.62974359, 21.62974359, 20.02083333, 20.02083333, 20.02083333,\n",
       "       20.02083333, 21.62974359, 20.02083333, 21.62974359, 21.62974359,\n",
       "       21.62974359, 21.62974359, 16.23896104, 21.62974359, 21.62974359,\n",
       "       16.23896104, 21.62974359, 16.23896104, 16.23896104, 16.23896104,\n",
       "       16.23896104, 16.23896104, 16.23896104, 21.62974359, 27.42727273,\n",
       "       16.23896104, 21.62974359, 16.23896104, 16.23896104, 16.23896104,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 27.42727273,\n",
       "       32.74878049, 27.42727273, 21.62974359, 21.62974359, 21.62974359,\n",
       "       21.62974359, 21.62974359, 20.02083333, 20.02083333, 20.02083333,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 20.02083333,\n",
       "       32.74878049, 21.62974359, 27.42727273, 21.62974359, 21.62974359,\n",
       "       21.62974359, 20.02083333, 21.62974359, 27.42727273, 32.74878049,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 21.62974359,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 21.62974359,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 21.62974359,\n",
       "       27.42727273, 27.42727273, 21.62974359, 21.62974359, 21.62974359,\n",
       "       27.42727273, 21.62974359, 21.62974359, 32.74878049, 32.74878049,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 21.62974359,\n",
       "       27.42727273, 21.62974359, 43.65333333, 43.65333333, 32.74878049,\n",
       "       27.42727273, 27.42727273, 21.62974359, 21.62974359, 21.62974359,\n",
       "       20.02083333, 20.02083333, 21.62974359, 21.62974359, 20.02083333,\n",
       "       21.62974359, 27.42727273, 16.23896104, 16.23896104, 21.62974359,\n",
       "       16.23896104, 21.62974359, 21.62974359, 16.23896104, 21.62974359,\n",
       "       21.62974359, 21.62974359, 16.23896104, 16.23896104, 16.23896104,\n",
       "       16.23896104, 16.23896104, 16.23896104, 16.23896104, 16.23896104,\n",
       "       21.62974359, 21.62974359, 21.62974359, 16.23896104, 16.23896104,\n",
       "       16.23896104, 16.23896104, 16.23896104, 16.23896104, 16.23896104,\n",
       "       16.23896104, 16.23896104, 16.23896104, 16.23896104, 16.23896104,\n",
       "       16.23896104, 16.23896104, 16.23896104, 16.23896104, 16.23896104,\n",
       "       21.62974359, 21.62974359, 21.62974359, 16.23896104, 16.23896104,\n",
       "       16.23896104, 16.23896104, 45.65      , 21.62974359, 21.62974359,\n",
       "       21.62974359, 48.3       , 48.3       , 48.3       , 21.62974359,\n",
       "       21.62974359, 48.3       , 21.62974359, 21.62974359, 21.62974359,\n",
       "       16.23896104, 21.62974359, 20.02083333, 21.62974359, 21.62974359,\n",
       "       27.42727273, 21.62974359, 21.62974359, 27.42727273, 32.74878049,\n",
       "       43.65333333, 21.62974359, 32.74878049, 27.42727273, 21.62974359,\n",
       "       21.62974359, 43.65333333, 27.42727273, 27.42727273, 32.74878049,\n",
       "       32.74878049, 27.42727273, 32.74878049, 27.42727273, 27.42727273,\n",
       "       48.3       , 32.74878049, 32.74878049, 32.74878049, 32.74878049,\n",
       "       32.74878049, 21.62974359, 48.3       , 48.3       , 48.3       ,\n",
       "       21.62974359, 21.62974359, 20.02083333, 20.02083333, 20.02083333,\n",
       "       20.02083333, 20.02083333, 20.02083333, 21.62974359, 20.02083333,\n",
       "       21.62974359, 21.62974359, 27.42727273, 16.23896104, 21.62974359,\n",
       "       32.74878049, 20.02083333, 27.42727273, 27.42727273, 43.65333333,\n",
       "       43.65333333, 43.65333333, 32.74878049, 43.65333333, 27.42727273,\n",
       "       21.62974359, 32.74878049, 43.65333333, 43.65333333, 27.42727273,\n",
       "       21.62974359, 27.42727273, 32.74878049, 21.62974359, 27.42727273,\n",
       "       27.42727273, 21.62974359, 21.62974359, 21.62974359, 21.62974359,\n",
       "       20.02083333, 21.62974359, 21.62974359, 21.62974359, 27.42727273,\n",
       "       21.62974359, 21.62974359, 32.74878049, 43.65333333, 21.62974359,\n",
       "       21.62974359, 43.65333333, 48.3       , 32.74878049, 27.42727273,\n",
       "       32.74878049, 48.3       , 48.3       , 32.74878049, 32.74878049,\n",
       "       21.62974359, 32.74878049, 48.3       , 48.3       , 21.62974359,\n",
       "       21.62974359, 21.62974359, 21.62974359, 43.65333333, 27.42727273,\n",
       "       27.42727273, 32.74878049, 27.42727273, 21.62974359, 27.42727273,\n",
       "       43.65333333, 32.74878049, 43.65333333, 48.3       , 32.74878049,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 27.42727273,\n",
       "       27.42727273, 32.74878049, 27.42727273, 21.62974359, 21.62974359,\n",
       "       27.42727273, 27.42727273, 20.02083333, 21.62974359, 32.74878049,\n",
       "       27.42727273, 27.42727273, 21.62974359, 32.74878049, 32.74878049,\n",
       "       27.42727273, 32.74878049, 27.42727273, 27.42727273, 21.62974359,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 27.42727273,\n",
       "       21.62974359, 16.23896104, 16.23896104, 21.62974359, 21.62974359,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 21.62974359,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 21.62974359,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 21.62974359,\n",
       "       21.62974359, 21.62974359, 21.62974359, 21.62974359, 21.62974359,\n",
       "       21.62974359, 32.74878049, 21.62974359, 27.42727273, 27.42727273,\n",
       "       21.62974359, 21.62974359, 21.62974359, 27.42727273, 27.42727273,\n",
       "       21.62974359, 27.42727273, 21.62974359, 27.42727273, 21.62974359,\n",
       "       21.62974359, 11.07741935, 21.62974359, 21.62974359, 21.62974359,\n",
       "       21.62974359, 21.62974359, 21.62974359, 16.23896104, 21.9       ,\n",
       "       21.62974359, 21.62974359, 21.62974359, 50.        , 50.        ,\n",
       "       45.65      , 50.        , 50.        , 11.07741935, 11.07741935,\n",
       "       16.4       , 11.07741935, 11.07741935, 11.07741935, 11.07741935,\n",
       "       10.4       , 11.07741935, 11.07741935, 11.07741935, 11.07741935,\n",
       "       11.07741935, 11.07741935, 11.07741935, 11.07741935, 11.07741935,\n",
       "       16.23896104, 16.23896104, 11.07741935, 11.07741935, 11.07741935,\n",
       "       11.07741935, 16.23896104, 11.07741935, 11.07741935, 11.07741935,\n",
       "       11.07741935, 11.07741935, 11.07741935, 11.07741935, 11.07741935,\n",
       "       11.07741935, 11.07741935, 27.9       , 16.63333333, 16.63333333,\n",
       "       21.62974359, 16.63333333, 16.63333333, 16.63333333, 11.07741935,\n",
       "       11.07741935, 11.07741935, 11.07741935, 11.07741935, 11.07741935,\n",
       "       11.07741935, 11.07741935, 21.62974359, 11.07741935, 16.63333333,\n",
       "       11.07741935, 16.63333333, 11.07741935, 11.07741935, 11.07741935,\n",
       "       16.63333333, 16.63333333, 21.62974359, 16.23896104, 11.07741935,\n",
       "       11.07741935, 11.07741935, 11.07741935, 11.07741935, 11.07741935,\n",
       "       11.07741935, 11.07741935, 16.23896104, 11.07741935, 11.07741935,\n",
       "       11.07741935, 16.23896104, 11.07741935, 11.07741935, 11.07741935,\n",
       "       16.23896104, 16.23896104, 16.23896104, 16.4       , 11.07741935,\n",
       "       16.23896104, 16.23896104, 11.07741935, 11.07741935, 16.23896104,\n",
       "       16.23896104, 16.23896104, 21.62974359, 21.62974359, 21.62974359,\n",
       "       21.62974359, 16.23896104, 16.23896104, 16.63333333, 16.63333333,\n",
       "       16.23896104, 21.62974359, 21.62974359, 32.74878049, 16.63333333,\n",
       "       16.23896104, 16.23896104, 11.07741935, 11.07741935, 21.62974359,\n",
       "       21.62974359, 27.42727273, 32.74878049, 21.62974359, 21.62974359,\n",
       "       21.62974359, 16.23896104, 21.62974359, 16.23896104, 16.23896104,\n",
       "       16.23896104, 16.23896104, 21.62974359, 21.62974359, 21.62974359,\n",
       "       16.23896104, 16.23896104, 21.62974359, 21.62974359, 16.23896104,\n",
       "       21.62974359, 27.42727273, 21.62974359, 32.74878049, 27.42727273,\n",
       "       21.62974359])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs.predict(boston_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 波士顿房价预测-随机森林算法-回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston_house = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_feature_name = boston_house.feature_names\n",
    "boston_features = boston_house.data\n",
    "boston_target = boston_house.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_feature_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class RandomForestRegressor in module sklearn.ensemble.forest:\n",
      "\n",
      "class RandomForestRegressor(ForestRegressor)\n",
      " |  A random forest regressor.\n",
      " |  \n",
      " |  A random forest is a meta estimator that fits a number of classifying\n",
      " |  decision trees on various sub-samples of the dataset and uses averaging\n",
      " |  to improve the predictive accuracy and control over-fitting.\n",
      " |  The sub-sample size is always the same as the original\n",
      " |  input sample size but the samples are drawn with replacement if\n",
      " |  `bootstrap=True` (default).\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <forest>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  n_estimators : integer, optional (default=10)\n",
      " |      The number of trees in the forest.\n",
      " |  \n",
      " |      .. versionchanged:: 0.20\n",
      " |         The default value of ``n_estimators`` will change from 10 in\n",
      " |         version 0.20 to 100 in version 0.22.\n",
      " |  \n",
      " |  criterion : string, optional (default=\"mse\")\n",
      " |      The function to measure the quality of a split. Supported criteria\n",
      " |      are \"mse\" for the mean squared error, which is equal to variance\n",
      " |      reduction as feature selection criterion, and \"mae\" for the mean\n",
      " |      absolute error.\n",
      " |  \n",
      " |      .. versionadded:: 0.18\n",
      " |         Mean Absolute Error (MAE) criterion.\n",
      " |  \n",
      " |  max_depth : integer or None, optional (default=None)\n",
      " |      The maximum depth of the tree. If None, then nodes are expanded until\n",
      " |      all leaves are pure or until all leaves contain less than\n",
      " |      min_samples_split samples.\n",
      " |  \n",
      " |  min_samples_split : int, float, optional (default=2)\n",
      " |      The minimum number of samples required to split an internal node:\n",
      " |  \n",
      " |      - If int, then consider `min_samples_split` as the minimum number.\n",
      " |      - If float, then `min_samples_split` is a fraction and\n",
      " |        `ceil(min_samples_split * n_samples)` are the minimum\n",
      " |        number of samples for each split.\n",
      " |  \n",
      " |      .. versionchanged:: 0.18\n",
      " |         Added float values for fractions.\n",
      " |  \n",
      " |  min_samples_leaf : int, float, optional (default=1)\n",
      " |      The minimum number of samples required to be at a leaf node.\n",
      " |      A split point at any depth will only be considered if it leaves at\n",
      " |      least ``min_samples_leaf`` training samples in each of the left and\n",
      " |      right branches.  This may have the effect of smoothing the model,\n",
      " |      especially in regression.\n",
      " |  \n",
      " |      - If int, then consider `min_samples_leaf` as the minimum number.\n",
      " |      - If float, then `min_samples_leaf` is a fraction and\n",
      " |        `ceil(min_samples_leaf * n_samples)` are the minimum\n",
      " |        number of samples for each node.\n",
      " |  \n",
      " |      .. versionchanged:: 0.18\n",
      " |         Added float values for fractions.\n",
      " |  \n",
      " |  min_weight_fraction_leaf : float, optional (default=0.)\n",
      " |      The minimum weighted fraction of the sum total of weights (of all\n",
      " |      the input samples) required to be at a leaf node. Samples have\n",
      " |      equal weight when sample_weight is not provided.\n",
      " |  \n",
      " |  max_features : int, float, string or None, optional (default=\"auto\")\n",
      " |      The number of features to consider when looking for the best split:\n",
      " |  \n",
      " |      - If int, then consider `max_features` features at each split.\n",
      " |      - If float, then `max_features` is a fraction and\n",
      " |        `int(max_features * n_features)` features are considered at each\n",
      " |        split.\n",
      " |      - If \"auto\", then `max_features=n_features`.\n",
      " |      - If \"sqrt\", then `max_features=sqrt(n_features)`.\n",
      " |      - If \"log2\", then `max_features=log2(n_features)`.\n",
      " |      - If None, then `max_features=n_features`.\n",
      " |  \n",
      " |      Note: the search for a split does not stop until at least one\n",
      " |      valid partition of the node samples is found, even if it requires to\n",
      " |      effectively inspect more than ``max_features`` features.\n",
      " |  \n",
      " |  max_leaf_nodes : int or None, optional (default=None)\n",
      " |      Grow trees with ``max_leaf_nodes`` in best-first fashion.\n",
      " |      Best nodes are defined as relative reduction in impurity.\n",
      " |      If None then unlimited number of leaf nodes.\n",
      " |  \n",
      " |  min_impurity_decrease : float, optional (default=0.)\n",
      " |      A node will be split if this split induces a decrease of the impurity\n",
      " |      greater than or equal to this value.\n",
      " |  \n",
      " |      The weighted impurity decrease equation is the following::\n",
      " |  \n",
      " |          N_t / N * (impurity - N_t_R / N_t * right_impurity\n",
      " |                              - N_t_L / N_t * left_impurity)\n",
      " |  \n",
      " |      where ``N`` is the total number of samples, ``N_t`` is the number of\n",
      " |      samples at the current node, ``N_t_L`` is the number of samples in the\n",
      " |      left child, and ``N_t_R`` is the number of samples in the right child.\n",
      " |  \n",
      " |      ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n",
      " |      if ``sample_weight`` is passed.\n",
      " |  \n",
      " |      .. versionadded:: 0.19\n",
      " |  \n",
      " |  min_impurity_split : float, (default=1e-7)\n",
      " |      Threshold for early stopping in tree growth. A node will split\n",
      " |      if its impurity is above the threshold, otherwise it is a leaf.\n",
      " |  \n",
      " |      .. deprecated:: 0.19\n",
      " |         ``min_impurity_split`` has been deprecated in favor of\n",
      " |         ``min_impurity_decrease`` in 0.19. The default value of\n",
      " |         ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it\n",
      " |         will be removed in 0.25. Use ``min_impurity_decrease`` instead.\n",
      " |  \n",
      " |  bootstrap : boolean, optional (default=True)\n",
      " |      Whether bootstrap samples are used when building trees.\n",
      " |  \n",
      " |  oob_score : bool, optional (default=False)\n",
      " |      whether to use out-of-bag samples to estimate\n",
      " |      the R^2 on unseen data.\n",
      " |  \n",
      " |  n_jobs : int or None, optional (default=None)\n",
      " |      The number of jobs to run in parallel for both `fit` and `predict`.\n",
      " |      `None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
      " |      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
      " |      for more details.\n",
      " |  \n",
      " |  random_state : int, RandomState instance or None, optional (default=None)\n",
      " |      If int, random_state is the seed used by the random number generator;\n",
      " |      If RandomState instance, random_state is the random number generator;\n",
      " |      If None, the random number generator is the RandomState instance used\n",
      " |      by `np.random`.\n",
      " |  \n",
      " |  verbose : int, optional (default=0)\n",
      " |      Controls the verbosity when fitting and predicting.\n",
      " |  \n",
      " |  warm_start : bool, optional (default=False)\n",
      " |      When set to ``True``, reuse the solution of the previous call to fit\n",
      " |      and add more estimators to the ensemble, otherwise, just fit a whole\n",
      " |      new forest. See :term:`the Glossary <warm_start>`.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  estimators_ : list of DecisionTreeRegressor\n",
      " |      The collection of fitted sub-estimators.\n",
      " |  \n",
      " |  feature_importances_ : array of shape = [n_features]\n",
      " |      The feature importances (the higher, the more important the feature).\n",
      " |  \n",
      " |  n_features_ : int\n",
      " |      The number of features when ``fit`` is performed.\n",
      " |  \n",
      " |  n_outputs_ : int\n",
      " |      The number of outputs when ``fit`` is performed.\n",
      " |  \n",
      " |  oob_score_ : float\n",
      " |      Score of the training dataset obtained using an out-of-bag estimate.\n",
      " |  \n",
      " |  oob_prediction_ : array of shape = [n_samples]\n",
      " |      Prediction computed with out-of-bag estimate on the training set.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from sklearn.ensemble import RandomForestRegressor\n",
      " |  >>> from sklearn.datasets import make_regression\n",
      " |  \n",
      " |  >>> X, y = make_regression(n_features=4, n_informative=2,\n",
      " |  ...                        random_state=0, shuffle=False)\n",
      " |  >>> regr = RandomForestRegressor(max_depth=2, random_state=0,\n",
      " |  ...                              n_estimators=100)\n",
      " |  >>> regr.fit(X, y)\n",
      " |  RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=2,\n",
      " |             max_features='auto', max_leaf_nodes=None,\n",
      " |             min_impurity_decrease=0.0, min_impurity_split=None,\n",
      " |             min_samples_leaf=1, min_samples_split=2,\n",
      " |             min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
      " |             oob_score=False, random_state=0, verbose=0, warm_start=False)\n",
      " |  >>> print(regr.feature_importances_)\n",
      " |  [0.18146984 0.81473937 0.00145312 0.00233767]\n",
      " |  >>> print(regr.predict([[0, 0, 0, 0]]))\n",
      " |  [-8.32987858]\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  The default values for the parameters controlling the size of the trees\n",
      " |  (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n",
      " |  unpruned trees which can potentially be very large on some data sets. To\n",
      " |  reduce memory consumption, the complexity and size of the trees should be\n",
      " |  controlled by setting those parameter values.\n",
      " |  \n",
      " |  The features are always randomly permuted at each split. Therefore,\n",
      " |  the best found split may vary, even with the same training data,\n",
      " |  ``max_features=n_features`` and ``bootstrap=False``, if the improvement\n",
      " |  of the criterion is identical for several splits enumerated during the\n",
      " |  search of the best split. To obtain a deterministic behaviour during\n",
      " |  fitting, ``random_state`` has to be fixed.\n",
      " |  \n",
      " |  The default value ``max_features=\"auto\"`` uses ``n_features`` \n",
      " |  rather than ``n_features / 3``. The latter was originally suggested in\n",
      " |  [1], whereas the former was more recently justified empirically in [2].\n",
      " |  \n",
      " |  References\n",
      " |  ----------\n",
      " |  \n",
      " |  .. [1] L. Breiman, \"Random Forests\", Machine Learning, 45(1), 5-32, 2001.\n",
      " |  \n",
      " |  .. [2] P. Geurts, D. Ernst., and L. Wehenkel, \"Extremely randomized \n",
      " |         trees\", Machine Learning, 63(1), 3-42, 2006.\n",
      " |  \n",
      " |  See also\n",
      " |  --------\n",
      " |  DecisionTreeRegressor, ExtraTreesRegressor\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      RandomForestRegressor\n",
      " |      ForestRegressor\n",
      " |      abc.NewBase\n",
      " |      BaseForest\n",
      " |      abc.NewBase\n",
      " |      sklearn.ensemble.base.BaseEnsemble\n",
      " |      abc.NewBase\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      sklearn.base.MetaEstimatorMixin\n",
      " |      sklearn.base.RegressorMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, n_estimators='warn', criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from ForestRegressor:\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Predict regression target for X.\n",
      " |      \n",
      " |      The predicted regression target of an input sample is computed as the\n",
      " |      mean predicted regression targets of the trees in the forest.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix of shape = [n_samples, n_features]\n",
      " |          The input samples. Internally, its dtype will be converted to\n",
      " |          ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
      " |          converted into a sparse ``csr_matrix``.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y : array of shape = [n_samples] or [n_samples, n_outputs]\n",
      " |          The predicted values.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from BaseForest:\n",
      " |  \n",
      " |  apply(self, X)\n",
      " |      Apply trees in the forest to X, return leaf indices.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix, shape = [n_samples, n_features]\n",
      " |          The input samples. Internally, its dtype will be converted to\n",
      " |          ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
      " |          converted into a sparse ``csr_matrix``.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_leaves : array_like, shape = [n_samples, n_estimators]\n",
      " |          For each datapoint x in X and for each tree in the forest,\n",
      " |          return the index of the leaf x ends up in.\n",
      " |  \n",
      " |  decision_path(self, X)\n",
      " |      Return the decision path in the forest\n",
      " |      \n",
      " |      .. versionadded:: 0.18\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix, shape = [n_samples, n_features]\n",
      " |          The input samples. Internally, its dtype will be converted to\n",
      " |          ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
      " |          converted into a sparse ``csr_matrix``.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      indicator : sparse csr array, shape = [n_samples, n_nodes]\n",
      " |          Return a node indicator matrix where non zero elements\n",
      " |          indicates that the samples goes through the nodes.\n",
      " |      \n",
      " |      n_nodes_ptr : array of size (n_estimators + 1, )\n",
      " |          The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]\n",
      " |          gives the indicator value for the i-th estimator.\n",
      " |  \n",
      " |  fit(self, X, y, sample_weight=None)\n",
      " |      Build a forest of trees from the training set (X, y).\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix of shape = [n_samples, n_features]\n",
      " |          The training input samples. Internally, its dtype will be converted\n",
      " |          to ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
      " |          converted into a sparse ``csc_matrix``.\n",
      " |      \n",
      " |      y : array-like, shape = [n_samples] or [n_samples, n_outputs]\n",
      " |          The target values (class labels in classification, real numbers in\n",
      " |          regression).\n",
      " |      \n",
      " |      sample_weight : array-like, shape = [n_samples] or None\n",
      " |          Sample weights. If None, then samples are equally weighted. Splits\n",
      " |          that would create child nodes with net zero or negative weight are\n",
      " |          ignored while searching for a split in each node. In the case of\n",
      " |          classification, splits are also ignored if they would result in any\n",
      " |          single class carrying a negative weight in either child node.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from BaseForest:\n",
      " |  \n",
      " |  feature_importances_\n",
      " |      Return the feature importances (the higher, the more important the\n",
      " |         feature).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      feature_importances_ : array, shape = [n_features]\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.ensemble.base.BaseEnsemble:\n",
      " |  \n",
      " |  __getitem__(self, index)\n",
      " |      Returns the index'th estimator in the ensemble.\n",
      " |  \n",
      " |  __iter__(self)\n",
      " |      Returns iterator over estimators in the ensemble.\n",
      " |  \n",
      " |  __len__(self)\n",
      " |      Returns the number of estimators in the ensemble.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : boolean, optional\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.RegressorMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Returns the coefficient of determination R^2 of the prediction.\n",
      " |      \n",
      " |      The coefficient R^2 is defined as (1 - u/v), where u is the residual\n",
      " |      sum of squares ((y_true - y_pred) ** 2).sum() and v is the total\n",
      " |      sum of squares ((y_true - y_true.mean()) ** 2).sum().\n",
      " |      The best possible score is 1.0 and it can be negative (because the\n",
      " |      model can be arbitrarily worse). A constant model that always\n",
      " |      predicts the expected value of y, disregarding the input features,\n",
      " |      would get a R^2 score of 0.0.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape = (n_samples, n_features)\n",
      " |          Test samples. For some estimators this may be a\n",
      " |          precomputed kernel matrix instead, shape = (n_samples,\n",
      " |          n_samples_fitted], where n_samples_fitted is the number of\n",
      " |          samples used in the fitting for the estimator.\n",
      " |      \n",
      " |      y : array-like, shape = (n_samples) or (n_samples, n_outputs)\n",
      " |          True values for X.\n",
      " |      \n",
      " |      sample_weight : array-like, shape = [n_samples], optional\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          R^2 of self.predict(X) wrt. y.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(RandomForestRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgs = RandomForestRegressor(n_estimators=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rgs = rgs.fit(boston_features,boston_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=15, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25.64666667, 21.34      , 34.26      , 33.22666667, 35.8       ,\n",
       "       26.86666667, 21.44666667, 22.4       , 16.39333333, 19.05333333,\n",
       "       17.53333333, 20.        , 21.40666667, 20.25333333, 18.46      ,\n",
       "       19.92666667, 22.86      , 17.72666667, 19.59333333, 18.58666667,\n",
       "       13.98666667, 19.48      , 15.08      , 14.88666667, 15.54666667,\n",
       "       14.31333333, 16.64      , 14.98666667, 18.71333333, 21.83333333])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs.predict(boston_features)[0:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 普通回归树建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgs2 = tree.DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgs2 = rgs2.fit(boston_features,boston_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgs2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pr1 = rgs2.predict(boston_features)[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr2 = rgs.predict(boston_features)[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25.64666667, 21.34      , 34.26      , 33.22666667, 35.8       ,\n",
       "       26.86666667, 21.44666667, 22.4       , 16.39333333, 19.05333333,\n",
       "       17.53333333, 20.        , 21.40666667, 20.25333333, 18.46      ,\n",
       "       19.92666667, 22.86      , 17.72666667, 19.59333333, 18.58666667,\n",
       "       13.98666667, 19.48      , 15.08      , 14.88666667, 15.54666667,\n",
       "       14.31333333, 16.64      , 14.98666667, 18.71333333, 21.83333333,\n",
       "       13.25333333, 16.00666667, 13.92666667, 14.26      , 14.08666667,\n",
       "       19.40666667, 20.29333333, 20.93333333, 23.14666667, 29.69333333,\n",
       "       35.34666667, 27.29333333, 24.42666667, 24.39333333, 21.22      ,\n",
       "       19.38666667, 20.03333333, 17.70666667, 15.20666667, 19.76      ])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 可视化比较随机森林和普通回归树的预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k8QT9ZLhhqyiGELhgyAUcKTnCH9f+kfOmxnHJ4sXw7rt8En6EWMKY3Hdynf0S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\nRqOxCf8PDU63Z7dYQLsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(50)\n",
    "plt.plot(x,pr1,'r',label='normal decison tree regreesor')\n",
    "plt.plot(x,pr2,'g',label=\"random forest\")\n",
    "plt.legend()\n",
    "plt.xlabel('sample index')\n",
    "plt.ylabel('price prediction')\n",
    "plt.title('comparision of normal decision tree regresor and Random Forest')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
